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Use these libraries to find Relation models and implementations
4 papers

Most implemented papers

Learning to Compare: Relation Network for Few-Shot Learning

floodsung/LearningToCompare_FSL CVPR 2018

Once trained, a RN is able to classify images of new classes by computing relation scores between query images and the few examples of each new class without further updating the network.

Matching the Blanks: Distributional Similarity for Relation Learning

plkmo/BERT-Relation-Extraction ACL 2019

General purpose relation extractors, which can model arbitrary relations, are a core aspiration in information extraction.

RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space

DeepGraphLearning/KnowledgeGraphEmbedding ICLR 2019

We study the problem of learning representations of entities and relations in knowledge graphs for predicting missing links.

Inductive Relation Prediction by Subgraph Reasoning

kkteru/grail ICML 2020

The dominant paradigm for relation prediction in knowledge graphs involves learning and operating on latent representations (i. e., embeddings) of entities and relations.

OCNet: Object Context Network for Scene Parsing

PkuRainBow/OCNet 4 Sep 2018

To capture richer context information, we further combine our interlaced sparse self-attention scheme with the conventional multi-scale context schemes including pyramid pooling~\citep{zhao2017pyramid} and atrous spatial pyramid pooling~\citep{chen2018deeplab}.

Graph WaveNet for Deep Spatial-Temporal Graph Modeling

nnzhan/Graph-WaveNet 31 May 2019

Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system.

Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning

shehzaadzd/MINERVA ICLR 2018

Knowledge bases (KB), both automatically and manually constructed, are often incomplete --- many valid facts can be inferred from the KB by synthesizing existing information.

Neural Motifs: Scene Graph Parsing with Global Context

rowanz/neural-motifs CVPR 2018

We then introduce Stacked Motif Networks, a new architecture designed to capture higher order motifs in scene graphs that further improves over our strong baseline by an average 7. 1% relative gain.

Relation Networks for Object Detection

msracver/Relation-Networks-for-Object-Detection CVPR 2018

Although it is well believed for years that modeling relations between objects would help object recognition, there has not been evidence that the idea is working in the deep learning era.

Joint entity recognition and relation extraction as a multi-head selection problem

bekou/multihead_joint_entity_relation_extraction 20 Apr 2018

State-of-the-art models for joint entity recognition and relation extraction strongly rely on external natural language processing (NLP) tools such as POS (part-of-speech) taggers and dependency parsers.